A Survival Strategy: What Can Regression Analysis Do For You?

“Monitoring is not an option”- a quote from a speaker at the recent American Bankers’ Association (ABA) fair lending conference in reference to fair lending monitoring.

In today’s fair lending compliance environment, compliance officers are besieged with new regulations as well as new requirements for old regulations. How do you survive? Multiple strategies are probably needed, one of which should be leveraging your use of technology. Specifically, with respect to fair lending monitoring, this means increasing your use of PC-based data analyses.

At one level, the analysis focuses on computing totals, averages and cross-tabbing HMDA data. Beyond this initial layer of scrutiny a more sophisticated approach, regression analysis, is often warranted. In the past, regression analysis was considered to be a “big bank” tool and not useful to smaller institutions. In today’s data-driven world, regression analysis is used by regulators to identify fair lending issues at increasingly smaller banks. As an example, the regulatory practice referred to as the “30, 30, 100“ rule, suggests that regulators may use regression analysis anytime there is a data sample that contains 30 prohibited basis applicants, 30 non-prohibited basis applicants and at least 100 applications in the total sample.

While smaller institutions have historically avoided regression analysis, the reality is in our new regulatory environment, today’s examiners are applying regression techniques to their fair lending reviews at much smaller institutions and we need to be better prepared. Beyond the factors discussed above, there are seven ‘Best Practice’ reasons many institutions should consider regression analysis as part of their survival kit aimed at meeting the growing regulatory burden. These rationales have been discussed previously in a presentation Part 2 Combined Fair Lending Modeling Series: Beyond Risk Assessments (see www.preissco.com/blog) and in ABA’s ToolBox 7 (see www.aba.com/Tools/Toolboxes/Pages/default.aspx) but bear repeating in this regulatory environment. Thus the benefits of using regression analysis are:

Complete Analysis – Regression analysis statistically reviews all files, not just a sample, thereby producing a more complete analysis and minimizing the chance of a regulatory surprise.

Outlier Identification – Identifies specific loan applications (often called outliers or exceptions) that may need to be manually reviewed to ensure the credit or pricing decision made can be justified by the contents in the applicant’s file.

Comparable Matches – Matches each outlier/exception applicant with similarly-qualified applicant(s). Matching apples to the nearest statistical apple eliminates the need for time consuming data searches looking for comparables. And, you may find that your outlier has several similar applicants that were priced lower for example than your exception applicant. In essence, understand what your loan records are telling the regulators and know which applications may draw their attention or garner questions. Knowing this will allow you to investigate internally first.

Guesswork Eliminated – Because the matching algorithm that is part of the regression process uses pre-defined criteria, it eliminates the guesswork caused by human judgment inherent in the typical side-by-side analysis when deciding whether an applicant is really comparable to another.

Reproducible Process – Furthermore, the regression process is easily reproducible and therefore generates more consistent results. It does not matter which loan or risk officer performs the analysis, you have the confidence of knowing that the process will executed the same way every time. Hence your results will be more consistent from one time period to the next.

Highlights Important Underwriting and Pricing Factors – Regression analysis should be based on your bank’s underwriting and pricing guidelines. The statistics associated with the analysis will tell you which of those factors are the most important and therefore, for example, should be the focus of your data accuracy and integrity efforts. For example, in a credit decision analysis, credit scores almost always turn out to be important, but length of time in a residence does not.

Cost Effective – Finally, and important to you, regression analysis is cost effective. It can cut your fair lending review costs by reducing the number of applications requiring manual file review.

In conclusion, if you are not already using regression analysis, I urge you to spend some time understanding how your bank may benefit from it. If you have questions or comments concerning the above, please feel free to contact me directly, (847) 295-6881.